- Segmentation/Classification of:
- White Matter Lesions in Multiple Sclerosis
- Brain vs. Skull (CT)
- Brain Hemorrhage/Stroke (CT)
- R Package Development/“Data Science”
- Neuroimaging and R (Neuroconductor Project)
April 30, 2018
Multiple pieces of software used
Lower the bar to entry
Complete pipeline
| Variable | Overall |
|---|---|
| n | 30 |
| Age (mean (sd)) | 39.27 (10.12) |
| sex = M (%) | 7 (23.3) |
| EDSS (mean (sd)) | 2.61 (1.88) |
| Lesion_Volume (mean (sd)) | 17.40 (16.13) |
| MS_Subtype (%) | |
| Clinically Isolated Syndrome | 2 (6.7) |
| Progressive-relapsing | 1 (3.3) |
| Relapsing-remitting | 24 (80.0) |
| Secondary-progressive | 2 (6.7) |
| Unspecified | 1 (3.3) |

Figure from Multi-Atlas Skull Stripping method paper (Doshi et al. 2013):

smri.process packageTraining Data Structure
Let \(y_{i}(v)\) be the presence / absence of lesion for voxel \(v\) from person \(i\).
General model form: \[ P(Y_{i}(v) = 1) \propto f(X_{i}(v)) \]
For each test scan, and over all voxels, we can calculate the following 2-by-2 table, where the cells represent number of voxels and a corresponding Venn diagram:
| Manual | |||
| 0 | 1 | ||
| PitCH | 0 | TN | FN |
| 1 | FP | TP | |
(Muschelli, John, et al. “fslr: Connecting the FSL Software with R.” R JOURNAL 7.1 (2015): 163-175.)
(Muschelli, John, Elizabeth Sweeney, and Ciprian Crainiceanu. “brainR: Interactive 3 and 4D Images of High Resolution Neuroimage Data.” R JOURNAL 6.1 (2014): 42-48.)
Muschelli, John, et al. “PItcHPERFeCT: Primary intracranial hemorrhage probability estimation using random forests on CT.” NeuroImage: Clinical 14 (2017): 379-390.
From the cranlogs R package:
Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1). Springer:5–32.
Doshi, Jimit, Guray Erus, Yangming Ou, Bilwaj Gaonkar, and Christos Davatzikos. 2013. “Multi-Atlas Skull-Stripping.” Academic Radiology 20 (12). Elsevier:1566–76.
Lesjak, Žiga, Alfiia Galimzianova, Aleš Koren, Matej Lukin, Franjo Pernuš, Boštjan Likar, and Žiga Špiclin. 2018. “A Novel Public MR Image Dataset of Multiple Sclerosis Patients with Lesion Segmentations Based on Multi-Rater Consensus.” Neuroinformatics 16 (1). Springer:51–63.
Sweeney, Elizabeth M, Russell T Shinohara, Navid Shiee, Farrah J Mateen, Avni A Chudgar, Jennifer L Cuzzocreo, Peter A Calabresi, Dzung L Pham, Daniel S Reich, and Ciprian M Crainiceanu. 2013. “OASIS Is Automated Statistical Inference for Segmentation, with Applications to Multiple Sclerosis Lesion Segmentation in MRI.” NeuroImage: Clinical 2. Elsevier:402–13.
Wright, Marvin N., and Andreas Ziegler. 2017. “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software 77 (1):1–17. https://doi.org/10.18637/jss.v077.i01.